Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Energy-Aware Data Aggregation Techniques in Wireless Sensor Network

Abstract: A Wireless Sensor Network (WSN) is an exigent technology and it has huge number of applications in disaster management, health monitoring, military, security, and so on. This network faces some critical barriers like fault tolerance, energy consumption due to heterogeneous traffic loads and redundant data transmission. In which, nodes are miniscule and have restricted capability of processing with reduced power of battery. This limitation of reduced power of battery makes the sensor network prone to failure. Data aggregation is a vital technique for active data processing in WSN. With the support of data aggregation, the energy depletion is minimized by eliminating redundant data or by decreasing the number of sent packets. This study reviews various data aggregation techniques such as clustered aggregation, tree-based aggregation, in-network aggregation, and centralized data aggregation with focus on energy consumption of sensor nodes.

Learning automata based energy efficient data aggregation in wireless sensor networks

Abstract: Recently, many algorithms have been proposed for data aggregation in wireless sensor networks which try to find routes towards the sink through which data can be aggregated. In addition to data aggregation, two more criteria are also used in many of these algorithms for finding the routes; remaining energies of the nodes and their numbers of hops to the sink. But to the best of our knowledge, no data aggregation algorithm has been presented in which all of these three criteria are considered together. In this paper, we propose a novel data aggregation algorithm, called LAG, which tries to mix all of these criteria for finding the routes. Furthermore, by considering the fact that the remaining energy of a sensor node and its possibility for aggregating data received from other nodes may change during the operation of the network, the proposed LAG algorithm tries to dynamically adapt itself with such changes and to select new routes towards the sink accordingly. The adaptive behavior of LAG is the result of using learning automata (LA). Each node is equipped with an LA which helps the node selects its next hop for forwarding data towards the sink considering all of the three mentioned criteria. The learning automaton used in LAG algorithm, called INCASE-LA, is introduced in this paper for the first time. Using computer simulations, we demonstrate that LAG aggregates data better, consumes less power and achieves higher network lifetime in comparison to other existing algorithms such as SPT, TAG, and ES LA.

Privacy-Preserving Smart Metering with Authentication in a Smart Grid

Abstract: The traditional security objectives of smart grids have been availability, integrity, and confidentiality. However, as the grids incorporate smart metering and load management, user and corporate privacy is increasingly becoming an issue in smart grid networks. Although transmitting current power consumption levels to the supplier or utility from each smart meter at short intervals has an advantage for the electricity supplier’s planning and management purposes, it threatens user privacy by disclosing fine-grained consumption data and usage behavior to utility providers. In this study, we propose a distributed incremental data aggregation scheme where all smart meters on an aggregation path are involved in routing the data from the source meter to the collection unit. User privacy is preserved by symmetric homomorphic encryption, which allows smart meters to participate in the aggregation without seeing any intermediate or final result. Aggregated data is further integrated with an aggregate signature to achieve data integrity and smart meter authentication in such a way that dishonest or fake smart meters cannot falsify data en route. Only the collection unit can obtain the aggregated data and verify its integrity while the individual plain data are not exposed to the collection unit. Therefore, user privacy and security are improved for the smart metering in a smart grid network.

Energy efficient aggregation technique-based realisable secure aware routing protocol for wireless sensor network

Abstract: Wireless sensor network (WSN) plays a predominant role from household equipment into military applications,sometimes utilised to handle most sensitive, personalised data like body sensors report, medical purposes in those cases, all kind of prescription and decisions are taken based on the sensor data. Many algorithms and techniques have been developed for these purposes among them trust-based algorithms outperform the conventional techniques. A trust method provides ultimate security with integrity, authenticity and availability of data to all the nodes. Data overhead is the complication encountered while implementation trust-based method which leads to complications like congestion into the system. And also directly affects the data aggregation process, as a cumulative effect, the overall lifetime of the network is drastically brought down. This study proposes a realisable secure aware routing (RSAR) protocol: RSAR method to overcome the said issue. The RSAR starts with the trust factor calculation of individual sensor node. Then the values are computed by optimal trust inference model using the conditional tug of war optimisation algorithm. The data aggregation help in reducing the instantaneous data flow of the individual node to multi-hop and filters out the necessary data alone, then transmits the accumulated information to the receiving end. The energy efficiency of this proposed work is achieved through bypassing the repeated and fault data from the gathered information and in turn, it will maximise the WSN lifetime by saving its battery. Trust factor calculations introduced by this method help in identifying the attacks and mitigate by calculating the trust factors.